🧠
Cellate Ner 2class Reinitllrd Tapt Pubmedbert Tokenizer Adapted Combdata Gazetteers Lr 3.36e5 model by Mardiyyah
⭐ 38.9
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Model

Cellate Ner 2class Reinitllrd Tapt Pubmedbert Tokenizer Adapted Combdata Gazetteers Lr 3.36e5

by Mardiyyah mardiyyah/cellate-ner-2class-reinitllrd-tapt-pubmedbert-tokenizer-adapted-combdata-gazetteers-lr_3.36e5
Free2AITools Nexus Index
38.9
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 1
R: Recency 96
Q: Quality 65
Tech Context
0.11B Params
512 Ctx
Vital Performance
12 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 38.9 FNI Score
Tiny 0.11B Params
1k Context
12 Downloads
8G GPU ~2GB Est. VRAM
Dense BERTFORTOKENCLASSIFICATION Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID mardiyyah/cellate-ner-2class-reinitllrd-tapt-pubmedbert-tokenizer-adapted-combdata-gazetteers-lr_3.36e5
License Apache-2.0
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.4GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{mardiyyah_cellate_ner_2class_reinitllrd_tapt_pubmedbert_tokenizer_adapted_combdata_gazetteers_lr_3_36e5,
  author = {Mardiyyah},
  title = {Cellate Ner 2class Reinitllrd Tapt Pubmedbert Tokenizer Adapted Combdata Gazetteers Lr 3.36e5 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/Mardiyyah/CeLLaTe-ner-2class-reinitllrd-tapt-pubmedbert-tokenizer-adapted-combData-gazetteers-lr_3.36e5}},
  note = {Accessed via Free2AITools.}
}
APA Style
Mardiyyah. (2026). Cellate Ner 2class Reinitllrd Tapt Pubmedbert Tokenizer Adapted Combdata Gazetteers Lr 3.36e5 [Model]. Free2AITools. https://huggingface.co/Mardiyyah/CeLLaTe-ner-2class-reinitllrd-tapt-pubmedbert-tokenizer-adapted-combData-gazetteers-lr_3.36e5

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run cellate-ner-2class-reinitllrd-tapt-pubmedbert-tokenizer-adapted-combdata-gazetteers-lr_3.36e5
πŸ€— HF Download
huggingface-cli download mardiyyah/cellate-ner-2class-reinitllrd-tapt-pubmedbert-tokenizer-adapted-combdata-gazetteers-lr_3.36e5
πŸ“¦ Install Lib
pip install -U transformers

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0
Popularity (P) 1
Recency (R) 96
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Cellate Ner 2class Reinitllrd Tapt Pubmedbert Tokenizer Adapted Combdata Gazetteers Lr 3.36e5: Authority (A:0), Popularity (P:1), Recency (R:96), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
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⚠️ Incomplete Data

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πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.

Social Proof

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πŸ€— Data Source: Hugging Face β†—
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Source summary: Based on Hugging Face metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-model--mardiyyah--cellate-ner-2class-reinitllrd-tapt-pubmedbert-tokenizer-adapted-combdata-gazetteers-lr_3.36e5
slug
mardiyyah--cellate-ner-2class-reinitllrd-tapt-pubmedbert-tokenizer-adapted-combdata-gazetteers-lr_3.36e5
source
huggingface
author
Mardiyyah
license
Apache-2.0
tags
transformers, safetensors, bert, token-classification, generated_from_trainer, en, license:apache-2.0, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
BertForTokenClassification
params billions
0.11
context length
512
pipeline tag
token-classification
vram gb
1.4
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
12

Data indexed from public sources. Updated daily.